Information overload is an obstacle in information retrieval systems. Users are faced with the problem of choosing between many possible resources those likely to satisfy their needs. Typical question answering (QA) systems take as input a question posed in natural language and respond with an automatically generated answer. As opposed to search engines that return a list of documents, web pages, links, images, etc., among which the answer is yet to be found, QA systems determine a direct answer to the posed question. Conventional open-domain QA systems derive the answer from unstructured data, e.g. documents, text corpus, file collections, etc.
Business Intelligence (BI) generally refers to a category of software systems and applications used to improve business enterprise decision-making and governance. These software tools provide techniques for analyzing and leveraging enterprise applications and data. Such advanced tools require some technical knowledge on how to formulate queries in order to retrieve relevant data. Typically, BI systems are based on structured data such as business domain models. Querying data warehouses requires training, technical knowledge and cannot readily be done. To access data managed by BI systems users typically enter technical queries expressed in a specific language, e.g., Structured Query Language (SQL), SPARQL Protocol and RDF Query Language (SPARQL), Multidimensional eXpressions (MDX) language, and the like. Such technical queries are not natural to non-expert users and have to be manually built by the users. Translating an arbitrary BI question expressed in natural language into relevant formal representation that leads to correct answer is not a trivial task.